{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning for Time Series Analysis. \n",
    "\n",
    "> LSTM : Long Short Term Memory Units (RNN species). \n",
    "\n",
    ">Divyanshu | Oil & Gas DS/ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:/Users/dvyas/UDEMY_TSA_FINAL/Data/airline_passengers.csv',index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index.freq = 'MS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b57ceb580>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df) - 24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.iloc[:120]\n",
    "test = df.iloc[120:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling : So the Error doesn't blow up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_train = scaler.transform(train)\n",
    "scaled_test = scaler.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Generation : An LSTM mandate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define generator\n",
    "n_input = 12\n",
    "n_features = 1\n",
    "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X0,y0 = generator[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 12, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X0 - IS called the BatchX and it is a 3D array. Much like LSTM accepts. \n",
    "# y0 - is the BatchY and issa 2D array. Even though it is potentially 1D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here's what a Batch looks like** - \n",
    "\n",
    "[[[ BatchX ]]] , [[Batchy]]\n",
    "\n",
    "i.e, ( 3D-X , 2D-y )\n",
    "\n",
    "The shape of 3d-X is - (1,n_input,n_features) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create and Compile & Fit LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define model\n",
    "model = Sequential()\n",
    "model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-28-590411636090>:2: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use Model.fit, which supports generators.\n",
      "Epoch 1/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0335\n",
      "Epoch 2/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0121\n",
      "Epoch 3/100\n",
      "108/108 [==============================] - 1s 13ms/step - loss: 0.0125\n",
      "Epoch 4/100\n",
      "108/108 [==============================] - 1s 13ms/step - loss: 0.0107\n",
      "Epoch 5/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0116\n",
      "Epoch 6/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0108\n",
      "Epoch 7/100\n",
      "108/108 [==============================] - 2s 20ms/step - loss: 0.0097\n",
      "Epoch 8/100\n",
      "108/108 [==============================] - 2s 16ms/step - loss: 0.0139\n",
      "Epoch 9/100\n",
      "108/108 [==============================] - 2s 21ms/step - loss: 0.0084: 0s - l\n",
      "Epoch 10/100\n",
      "108/108 [==============================] - 2s 21ms/step - loss: 0.0116: 0s - loss: 0.0\n",
      "Epoch 11/100\n",
      "108/108 [==============================] - 2s 21ms/step - loss: 0.0075\n",
      "Epoch 12/100\n",
      "108/108 [==============================] - 2s 21ms/step - loss: 0.0062\n",
      "Epoch 13/100\n",
      "108/108 [==============================] - 2s 21ms/step - loss: 0.0048\n",
      "Epoch 14/100\n",
      "108/108 [==============================] - 2s 21ms/step - loss: 0.0045\n",
      "Epoch 15/100\n",
      "108/108 [==============================] - 2s 21ms/step - loss: 0.0042\n",
      "Epoch 16/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0032: 0s - loss: 0.003\n",
      "Epoch 17/100\n",
      "108/108 [==============================] - 2s 16ms/step - loss: 0.0044: 0s - loss: 0.00\n",
      "Epoch 18/100\n",
      "108/108 [==============================] - 1s 13ms/step - loss: 0.0037\n",
      "Epoch 19/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0036: 0s - loss: 0.0\n",
      "Epoch 20/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0028\n",
      "Epoch 21/100\n",
      "108/108 [==============================] - 2s 20ms/step - loss: 0.0021: 1s - loss: - ETA: 0s - loss: 0.002 - ETA: 0s - loss: 0.002 - ETA: 0s - \n",
      "Epoch 22/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0030: 0s - l\n",
      "Epoch 23/100\n",
      "108/108 [==============================] - 2s 20ms/step - loss: 0.0021\n",
      "Epoch 24/100\n",
      "108/108 [==============================] - 2s 20ms/step - loss: 0.0019: 1s - loss: - ETA: 0\n",
      "Epoch 25/100\n",
      "108/108 [==============================] - 2s 20ms/step - loss: 0.0021\n",
      "Epoch 26/100\n",
      "108/108 [==============================] - 1s 11ms/step - loss: 0.0024: 0s - loss - ETA: 0s - loss\n",
      "Epoch 27/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0023: 0s - \n",
      "Epoch 28/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0025: 0s - los\n",
      "Epoch 29/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0019\n",
      "Epoch 30/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0019\n",
      "Epoch 31/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0027: 0s - loss\n",
      "Epoch 32/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0025\n",
      "Epoch 33/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0020\n",
      "Epoch 34/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0022\n",
      "Epoch 35/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0022\n",
      "Epoch 36/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0016\n",
      "Epoch 37/100\n",
      "108/108 [==============================] - 2s 16ms/step - loss: 0.0021\n",
      "Epoch 38/100\n",
      "108/108 [==============================] - 1s 14ms/step - loss: 0.0016\n",
      "Epoch 39/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0022\n",
      "Epoch 40/100\n",
      "108/108 [==============================] - 2s 16ms/step - loss: 0.0020\n",
      "Epoch 41/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0020\n",
      "Epoch 42/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0018\n",
      "Epoch 43/100\n",
      "108/108 [==============================] - 1s 14ms/step - loss: 0.0034\n",
      "Epoch 44/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0020\n",
      "Epoch 45/100\n",
      "108/108 [==============================] - 1s 14ms/step - loss: 0.0019\n",
      "Epoch 46/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0023\n",
      "Epoch 47/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0019: 1s\n",
      "Epoch 48/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0014: 0s - los\n",
      "Epoch 49/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0019\n",
      "Epoch 50/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0017: 0s - los\n",
      "Epoch 51/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0015\n",
      "Epoch 52/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0017\n",
      "Epoch 53/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0022\n",
      "Epoch 54/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0024\n",
      "Epoch 55/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0016\n",
      "Epoch 56/100\n",
      "108/108 [==============================] - 1s 13ms/step - loss: 0.0016\n",
      "Epoch 57/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0016: 0s - loss\n",
      "Epoch 58/100\n",
      "108/108 [==============================] - 1s 13ms/step - loss: 0.0016ETA\n",
      "Epoch 59/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0016\n",
      "Epoch 60/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0019: 1s -\n",
      "Epoch 61/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0016\n",
      "Epoch 62/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0014 ETA: 1s - loss:  - ETA: \n",
      "Epoch 63/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0017\n",
      "Epoch 64/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0015\n",
      "Epoch 65/100\n",
      "108/108 [==============================] - 2s 17ms/step - loss: 0.0018\n",
      "Epoch 66/100\n",
      "108/108 [==============================] - 1s 12ms/step - loss: 0.0016: 0s - \n",
      "Epoch 67/100\n",
      "108/108 [==============================] - 1s 12ms/step - loss: 0.0020\n",
      "Epoch 68/100\n",
      "108/108 [==============================] - 1s 12ms/step - loss: 0.0018\n",
      "Epoch 69/100\n",
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0017\n",
      "Epoch 70/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0018\n",
      "Epoch 71/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0015\n",
      "Epoch 72/100\n",
      "108/108 [==============================] - 1s 14ms/step - loss: 0.0016\n",
      "Epoch 73/100\n",
      "108/108 [==============================] - 1s 14ms/step - loss: 0.0015\n",
      "Epoch 74/100\n",
      "108/108 [==============================] - 1s 14ms/step - loss: 0.0013\n",
      "Epoch 75/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0015\n",
      "Epoch 76/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0016\n",
      "Epoch 77/100\n",
      "108/108 [==============================] - 1s 13ms/step - loss: 0.0016ETA: 1s - - ETA: 0s - los\n",
      "Epoch 78/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0013\n",
      "Epoch 79/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0014\n",
      "Epoch 80/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0018\n",
      "Epoch 81/100\n",
      "108/108 [==============================] - 2s 18ms/step - loss: 0.0013\n",
      "Epoch 82/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0016\n",
      "Epoch 83/100\n",
      "108/108 [==============================] - 1s 13ms/step - loss: 0.0017\n",
      "Epoch 84/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0017A:\n",
      "Epoch 85/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0015: 0s - \n",
      "Epoch 86/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0014\n",
      "Epoch 87/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0015\n",
      "Epoch 88/100\n",
      "108/108 [==============================] - 1s 12ms/step - loss: 0.0012\n",
      "Epoch 89/100\n",
      "108/108 [==============================] - 1s 12ms/step - loss: 0.0015\n",
      "Epoch 90/100\n",
      "108/108 [==============================] - 1s 12ms/step - loss: 0.0013\n",
      "Epoch 91/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108/108 [==============================] - 2s 14ms/step - loss: 0.0013: 1s - - ETA: 0s - loss\n",
      "Epoch 92/100\n",
      "108/108 [==============================] - 2s 20ms/step - loss: 0.0018: 1s - los - ETA: 0s\n",
      "Epoch 93/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0015\n",
      "Epoch 94/100\n",
      "108/108 [==============================] - 2s 15ms/step - loss: 0.0015\n",
      "Epoch 95/100\n",
      "108/108 [==============================] - 1s 14ms/step - loss: 0.0016\n",
      "Epoch 96/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0015: 1s - lo - E\n",
      "Epoch 97/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0018\n",
      "Epoch 98/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0012: 0s - loss: 0\n",
      "Epoch 99/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0012: 0s - \n",
      "Epoch 100/100\n",
      "108/108 [==============================] - 2s 19ms/step - loss: 0.0012\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x26b51605970>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit model\n",
    "model.fit_generator(generator,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26b686311f0>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3xV1Z3//9fnnNy4BBIgYEhAECKKIhcjotYWL9MCvaCtWvVXtdaO9Tv6rZ3ptOPM9Ddtf9OZcaz24tSR0eqordVatS22tKh4r6JcVK4C4Z4QIFySkHtO8vn9cXaSkxDIAQIJ2e/n43Ee5+y91z5Zi8t5Z6219zrm7oiISPhEeroCIiLSMxQAIiIhpQAQEQkpBYCISEgpAEREQkoBICISUkkFgJnNMrN1ZlZkZnd1ctzM7P7g+AozmxbszzCz98zsQzNbbWbfTzjne2ZWYmYfBI853dcsERHpSkpXBcwsCjwA/BVQDCwxs/nuviah2GygIHicDzwYPNcDl7p7lZmlAm+Z2Z/cfXFw3o/d/d7ua46IiCQrmR7AdKDI3Te5ewPwNDC3Q5m5wBMetxjIMrPcYLsqKJMaPHTnmYhIL9BlDwDIA7YnbBcT/+2+qzJ5QGnQg1gGjAcecPd3E8rdYWY3AkuBb7r7/sNVZNiwYT5mzJgkqiwiIi2WLVu2x91zOu5PJgCsk30df4s/ZBl3bwKmmFkW8FszO9vdVxEfJvrXoNy/AvcBXznoh5vdCtwKMHr0aJYuXZpElUVEpIWZbe1sfzJDQMXAqITtfGDHkZZx93LgNWBWsL3L3ZvcvRl4mPhQ00Hc/SF3L3T3wpycgwJMRESOUjIBsAQoMLOxZpYGXAvM71BmPnBjcDXQDKDC3UvNLCf4zR8z6wdcDnwUbOcmnH8lsOoY2yIiIkegyyEgd4+Z2R3AQiAKPOruq83stuD4PGABMAcoAmqAm4PTc4HHg3mACPCMu/8hOHaPmU0hPgS0Bfhat7VKRES6ZCfTctCFhYWuOQARkSNjZsvcvbDjft0JLCISUgoAEZGQUgCIiIRUKAJg0dpdPPjaxp6uhohIrxKKAHh9fRkPvaEAEBFJFIoASI1GaGw6ea52EhE5EUITAA1NzT1dDRGRXiUUAZAWNRqbmjmZ7nkQETneQhEAqdEI7tDUrAAQEWkRjgBIiTdT8wAiIm3CEQDReDM1DyAi0iYUAZDW2gNQAIiItAhHAETj31ejABARaROKAGgZAmqMaQ5ARKRFqAJAcwAiIm1CFQAaAhIRaROKAEhL0RyAiEhHoQgA9QBERA4WqgBo0CSwiEirUAWAegAiIm1CEQBpCgARkYOEIgBSNQksInKQpALAzGaZ2TozKzKzuzo5bmZ2f3B8hZlNC/ZnmNl7Zvahma02s+8nnDPEzF4ysw3Bc3b3Nau9tvsANAcgItKiywAwsyjwADAbmAhcZ2YTOxSbDRQEj1uBB4P99cCl7j4ZmALMMrMZwbG7gEXuXgAsCraPi9YhoJh6ACIiLZLpAUwHitx9k7s3AE8DczuUmQs84XGLgSwzyw22q4IyqcHDE855PHj9OHDFsTTkcDQJLCJysGQCIA/YnrBdHOxLqoyZRc3sA2A38JK7vxuUGeHupQDB8/Ajr35yUrUYnIjIQZIJAOtkX8fB9EOWcfcmd58C5APTzezsI6mgmd1qZkvNbGlZWdmRnNqq5QthNAcgItImmQAoBkYlbOcDO460jLuXA68Bs4Jdu8wsFyB43t3ZD3f3h9y90N0Lc3JykqjuwXQZqIjIwZIJgCVAgZmNNbM04Fpgfocy84Ebg6uBZgAV7l5qZjlmlgVgZv2Ay4GPEs65KXh9E/D7Y2zLIaVqElhE5CApXRVw95iZ3QEsBKLAo+6+2sxuC47PAxYAc4AioAa4OTg9F3g8uJIoAjzj7n8Ijt0NPGNmtwDbgKu7r1ntRSNGxNQDEBFJ1GUAALj7AuIf8on75iW8duD2Ts5bAUw9xHvuBS47ksoei9RoRHMAIiIJQnEnMMTnAdQDEBFpE5oASE1RAIiIJApPAERNASAikiBEARDR9wGIiCQITQBoDkBEpL3QBECqAkBEpJ3wBECK5gBERBKFJwB0H4CISDuhCgAtBSEi0iY0AaBJYBGR9kITALoPQESkvRAFgOYAREQShScAtBSEiEg7oQkAzQGIiLQXmgBIjZquAhIRSRCiANAcgIhIolAFgIaARETahCYA0jQJLCLSTmgCQPcBiIi0F6IAiNDY5MS/vlhEREIVAACNmggWEQGSDAAzm2Vm68ysyMzu6uS4mdn9wfEVZjYt2D/KzF41s7VmttrM7kw453tmVmJmHwSPOd3XrIOltQaAhoFERABSuipgZlHgAeCvgGJgiZnNd/c1CcVmAwXB43zgweA5BnzT3ZebWSawzMxeSjj3x+5+b/c159BSowYoAEREWiTTA5gOFLn7JndvAJ4G5nYoMxd4wuMWA1lmluvupe6+HMDdDwBrgbxurH/SUlPiTW3QzWAiIkByAZAHbE/YLubgD/Euy5jZGGAq8G7C7juCIaNHzSw7yToflZY5gAb1AEREgOQCwDrZ13Em9bBlzGwg8BzwDXevDHY/CIwDpgClwH2d/nCzW81sqZktLSsrS6K6nUvTJLCISDvJBEAxMCphOx/YkWwZM0sl/uH/pLs/31LA3Xe5e5O7NwMPEx9qOoi7P+Tuhe5emJOTk0R1O5eqSWARkXaSCYAlQIGZjTWzNOBaYH6HMvOBG4OrgWYAFe5eamYGPAKsdfcfJZ5gZrkJm1cCq466FUlomQTWHICISFyXVwG5e8zM7gAWAlHgUXdfbWa3BcfnAQuAOUARUAPcHJx+EXADsNLMPgj2/ZO7LwDuMbMpxIeKtgBf67ZWdaJlElg9ABGRuC4DACD4wF7QYd+8hNcO3N7JeW/R+fwA7n7DEdX0GGkOQESkvRDeCawegIgIhCoAgjkABYCICBCqAAh6AJoEFhEBQhQAaSmaAxARSRSaANAcgIhIeyEKAM0BiIgkCk0AaDloEZH2QhMAmgQWEWkvPAGgSWARkXbCEwCaAxARaSc8ARDRHICISKLQBEAkYqRETAEgIhIITQBAfCJYcwAiInEhCwDT9wGIiARCFQBpKRENAYmIBEIVAPEhIAWAiAiEMgA0ByAiAqELANN9ACIigZAFQERLQYiIBEIVAJoEFhFpE6oA0ByAiEibkAWA5gBERFokFQBmNsvM1plZkZnd1clxM7P7g+MrzGxasH+Umb1qZmvNbLWZ3ZlwzhAze8nMNgTP2d3XrM7pMlARkTZdBoCZRYEHgNnAROA6M5vYodhsoCB43Ao8GOyPAd909zOBGcDtCefeBSxy9wJgUbB9XKUpAEREWiXTA5gOFLn7JndvAJ4G5nYoMxd4wuMWA1lmluvupe6+HMDdDwBrgbyEcx4PXj8OXHGMbelS/CogzQGIiEByAZAHbE/YLqbtQzzpMmY2BpgKvBvsGuHupQDB8/BkK320UnUVkIhIq2QCwDrZ1/HX6MOWMbOBwHPAN9y9MvnqgZndamZLzWxpWVnZkZx6EE0Ci4i0SSYAioFRCdv5wI5ky5hZKvEP/yfd/fmEMrvMLDcokwvs7uyHu/tD7l7o7oU5OTlJVPfQNAcgItImmQBYAhSY2VgzSwOuBeZ3KDMfuDG4GmgGUOHupWZmwCPAWnf/USfn3BS8vgn4/VG3Ikm6D0BEpE1KVwXcPWZmdwALgSjwqLuvNrPbguPzgAXAHKAIqAFuDk6/CLgBWGlmHwT7/sndFwB3A8+Y2S3ANuDq7mtW57QUhIhImy4DACD4wF7QYd+8hNcO3N7JeW/R+fwA7r4XuOxIKnusUlM0ByAi0iJUdwJrDkBEpE2oAiA1GqHZoalZ8wAiIqELAEC9ABERQhcA8ekIzQOIiIQsANJSgh6ArgQSEQlXALQNAWkOQEQkpAGgHoCISMgCQHMAIiItQhUAaeoBiIi0ClUAtA4B6TsBRERCFgDBVUAaAhIRCVsABHMAGgISEQlZAGgOQESkTagCQJeBioi0CWUANGgSWEQkXAGQlqI5ABGRFqEKAA0BiYi0UQCIiIRUKAOgQYvBiYiEKwBaLwPVctAiIuEKgFRNAouItApXAGgOQESkVVIBYGazzGydmRWZ2V2dHDczuz84vsLMpiUce9TMdpvZqg7nfM/MSszsg+Ax59ibc3gpkWA5aA0BiYh0HQBmFgUeAGYDE4HrzGxih2KzgYLgcSvwYMKxx4BZh3j7H7v7lOCx4AjrfsTMjLRoRJPAIiIk1wOYDhS5+yZ3bwCeBuZ2KDMXeMLjFgNZZpYL4O5vAPu6s9LHIjVqGgISESG5AMgDtidsFwf7jrRMZ+4IhoweNbPszgqY2a1mttTMlpaVlSXxloeXlhJRAIiIkFwAWCf7Oo6hJFOmoweBccAUoBS4r7NC7v6Quxe6e2FOTk5Xde1SalQBICICyQVAMTAqYTsf2HEUZdpx913u3uTuzcDDxIeajrvUaESLwYmIkFwALAEKzGysmaUB1wLzO5SZD9wYXA00A6hw99LDvWnLHEHgSmDVocp2Jw0BiYjEpXRVwN1jZnYHsBCIAo+6+2ozuy04Pg9YAMwBioAa4OaW883sKWAmMMzMioHvuvsjwD1mNoX4UNEW4Gvd2K5D0iSwiEhclwEAEFyiuaDDvnkJrx24/RDnXneI/TckX83uozkAEZG4UN0JDMEcQDfcB+Du/OTl9WzZU90NtRIROfFCFwBp0Ui3LAa3o6KOn7y8gRc+POxct4hIrxW6AEhN6Z45gB3ltQDsqao/5vcSEekJ4QuAbpoDKNnfEgANx/xeIiI9IZQB0B1zACVBD6BMPQAROUmFLgDSuqsH0DIEdEABICInp9AFQHfdB7BDPQAROcmFMAC66SqgIAAO1MWoa2w65vcTETnRwhcAKcc+B+DulOyvZWB6/D46XQkkIiej0AVAyxxAfayJf/7tSj5+z6scqGs8oveorI1R3dDEpLzBAJRpHkBETkKhC4DUqFHb0MQ1897hyXe3sW1fDX8p2ntE79EyATx5VBagS0FF5OQUwgCI0NDUzKayah64fhoD01N4ff3uI3qPlgCYMko9ABE5eSW1GFxfcu6p2Vw4big/uOJsTssZyPwPS3h9XRnujlln32tzsJYJ4En5LT0ABYCInHxCFwCXnTmCy84c0bo9c8JwFq7exYbdVZw+IjOp9ygpryUtJULuoAwG90tVD0BETkqhGwLq6BOnx79m8vV1bd833NTsvLtpL83NnV8tVFJeS15WPyIRIycz/aAewM9e2cCbG479+4tFRI6n0AfAyKx+nD5iIK8lzAP896tFfPGhxTz85qZOz9lRXsvIrAwAhg1MaxcAjU3N/OTlDTyztPj4VlxE5BiFPgAg3gtYsnk/1fUxSspreeC1ItJSItz30no27DpwUPmS/fEeAEBOZka7IaCte2uINTsl+2tOWP1FRI6GAoD4PEBDUzPvbNzLv/1xDQDP3nYBA9NT+OZvPiSWsHREfayJ3QfqGRkEQLwH0HYZ6MayKqDtSiERkd5KAQAUjsmmf1qUnyxaz4KVO7l95njOyc/iB1eczYriCh58bWNr2Z0VdQCtAZCTmU5VfYzahvhyEC0BsPtAPQ3dsOSEiMjxogAA0lOiXDhuKKtKKhk9pD9//fHTAJgzKZfPTh7JTxdtaB0KavnNPr+1B5AOtF0KWrQ7HgDuUFqhXoCI9F4KgMClZ8QvDf2Xz0wkIzXauv/7nzuLlKjx8zc3A7Cj/OAeAMR/4wfYWFZNRmr8j7XlS2NERHqjpALAzGaZ2TozKzKzuzo5bmZ2f3B8hZlNSzj2qJntNrNVHc4ZYmYvmdmG4Dn72Jtz9K4pzOf3t1/E5RNHtNs/ZEAan5+Wz+8+KGFfdUPrh/opg+NXAeUk9ADcnU27qzh/7FAAijUPICK9WJcBYGZR4AFgNjARuM7MJnYoNhsoCB63Ag8mHHsMmNXJW98FLHL3AmBRsN1jUqKR1rV9OvryhWOojzXz1Hvb2FFeS05memsvoaUHUHagnrID9Ryoj3FxwTDM1AMQkd4tmR7AdKDI3Te5ewPwNDC3Q5m5wBMetxjIMrNcAHd/A9jXyfvOBR4PXj8OXHE0DTgRTh+RycfGD+MX72xl677q1uEfiPcQIN4DaBn/PzN3EMMz03UlkIj0askEQB6wPWG7ONh3pGU6GuHupQDB8/Ak6tJjbr5oDDsr61i8aR95wU1gEF9cLrt/Knuq6luvABqXM5C8rH7qAYhIr5ZMAHS2QlrHNRKSKXNUzOxWM1tqZkvLynpueYVLJgxnzND+AK03gbXIyUyn7EA9G8uqGZAWZcSgdPKy+6sHICK9WjIBUAyMStjOB3YcRZmOdrUMEwXPna7J7O4PuXuhuxfm5OQkUd3jIxIxbrpwDEC7ISCIXwq6p6qBjWVVjBs+EDMjL6sfpRW1h1xPSESkpyUTAEuAAjMba2ZpwLXA/A5l5gM3BlcDzQAqWoZ3DmM+cFPw+ibg90dQ7x5xTeEorpyax8wJ7UerWnsAu6sYlzMQgLzsfjQ2eevloSIivU2Xy0G7e8zM7gAWAlHgUXdfbWa3BcfnAQuAOUARUAPc3HK+mT0FzASGmVkx8F13fwS4G3jGzG4BtgFXd2fDjocB6Sn8+ItTDto/bGA6OyvqaGhqZlzOAKDtRrGS8prWS0ZFRHqTpL4PwN0XEP+QT9w3L+G1A7cf4tzrDrF/L3BZ0jXtxXIy02kI1gsaPzzeA8jPjgdA8f5azj21x6omInJIuhO4G7QsBwG0GwICLQonIr2XAqAbtNwMFo0Yo4MrhfqnpZDdP1WXgopIr6UA6AbDBsZvBhs9pD/pKW3rCOVl91MPQER6LQVAN2jpAbRMALfQzWAi0pspALrBkP5pZKRGmHBK+y+Vz8uK3wwWnyMXEeldkroKSA4vJRrhuf9zIaOH9G+3Py+7HzUNTZTXNJIdrBkkItJbqAfQTc4aOZjMjNR2+/KydCWQiPReCoDjKPFeABGR3kYBcBypByAivZkC4DjK6p9K/7SorgQSkV5JAXAcmRljhw1gZUl5T1dFROQgCoDj7NPn5LJky3627Knu6aqIiLSjADjOvjAtn4jBs8uKe7oqIiLtKACOsxGDMvj46Tk8t7yYJn05jIj0IgqAE+Dqc0dRWlHHX4r29HRVRERaKQBOgMsnDierfyq/0TCQiPQiCoATID0lyhVT8li4eicVNY09XR0REUABcMJcdW4+DbFm5n9Y0tNVEREBFAAnzNl5gzkzd5CGgUSk11AAnEBXnZvPiuIKNuw60NNVERFRAJxIc6eMJCViPLtcvQAR6XkKgBNo2MB0Zk4Yzm+XlxBrau7p6ohIyCUVAGY2y8zWmVmRmd3VyXEzs/uD4yvMbFpX55rZ98ysxMw+CB5zuqdJvdtV5+ax+0A9b+meABHpYV0GgJlFgQeA2cBE4Dozm9ih2GygIHjcCjyY5Lk/dvcpwWPBsTbmZHDpGSPI7p/Kc8t1NZCI9KxkegDTgSJ33+TuDcDTwNwOZeYCT3jcYiDLzHKTPDdU0lIifG7yyPg9AbW6J0BEek4yAZAHbE/YLg72JVOmq3PvCIaMHjWz7KRrfZL7QnBPwB9XlPZ0VUQkxJIJAOtkX8dVzQ5V5nDnPgiMA6YApcB9nf5ws1vNbKmZLS0rK0uiur3fpLzBnD5iIPcv2sBLa3bhrkXiROTESyYAioFRCdv5wI4kyxzyXHff5e5N7t4MPEx8uOgg7v6Quxe6e2FOTk4S1e39zIx7rprMwIwU/vqJpdzwyHus26l7A0TkxEomAJYABWY21szSgGuB+R3KzAduDK4GmgFUuHvp4c4N5ghaXAmsOsa2nFSmjMriT3dezPc+O5EVxeXMuf9Nvv/CairrOp8X2FfdwC/e2UKjLh8VkW6S0lUBd4+Z2R3AQiAKPOruq83stuD4PGABMAcoAmqAmw93bvDW95jZFOJDQluAr3Vnw04GqdEIX75oLHOn5PHDF9fx2NtbeOHDHXz3s2fx2ckj25W978V1PPnuNkor6vj2rDN6qMYi0pfYyTT+XFhY6EuXLu3pahw3K4sr+M7vVrKypIJF35zJ2GEDAKiobWTGvy8iGjGq6mM8/pXpfOL0vjEcJiLHn5ktc/fCjvt1J3AvMil/MD+/6TzSUiL81ysbWvc/t6yY2sYm/vfm85gwIpO/+/UH7Kqs68GaikhfoADoZXIy07lhxqn87v0SNpVV0dzs/GLxVqaNzuK8MUP42fVTqWlo4v8+9T4by6p0BZGIHDUFQC9068fHkZYS4WevFPFm0R4276nmpgvHAFAwIpMfXHE2S7bs47L7XufjP3yV77+wmqr6WM9WWkROOl1OAsuJ19ILeOStzazffYBhA9OZfXbbRVNfODef6WOH8Nr6Ml77aDePvb2FWJPzr1ec3YO17t3cnbrGZvqlRXu6KiK9hnoAvVRLL2BVSSXXTx9FWkr7v6pRQ/rHQ+LL53HzhWP5xeKtLN2yr4dq2/v9cvFWpv/7y+yvbujpqoj0GgqAXionM52bLhxDWkqE688/9bBlv/nJ08nL6sc/PLeC+lhTp2Uq6xppbg7vfMGzy0s4UBfjhRUd72EUCS8FQC/2rU9O4NW/n8kpgzMOW25Aegr/duXZbCyr5r9f3XjQ8Z0VdVz0H6/w00UbOjm77yveX8OH28sBeFZfySnSSgHQi6VEI+Rl9Uuq7MwJw7liykj++7Wig5aV+Omi9Ryoj/HQG5soO1B/PKraq/151U4AbrrgVFYUV2jZDZGAAqAP+X8/M5HB/VK58+n3qWuMDwUV7a7imaXF/NXEETQ0NfPAq0XHvR4riyu45n/e4XvzV7NgZSl7qno2dP60aicTcwfx9csKSIkYz+krOUUABUCfMnRgOj+8ajIf7TzADxeuA+Dehevolxrl7s9P4prCfJ58dyvF+2u6fK/3Nu/jz6tKW4MEYEd5Ld/53Upuf3L5IecaYk3NfPu5FazZUcnTS7bxN08uZ8a/L+KFD3tm7H1nRR3Ltu5nzqRTGDownUvOGM7zPfiVnAfqGimv0US09A66DLSPueSM4dx0QfwS0uz+qfx59U7+9vLTGTowna9fVsBzy0v4ycsbuPfqyZ2eX3agnn/9wxrmBx/Yg/ulcuXUPJrdefq97ThOY5MzID3Kf37hHMzar/j92NtbWFtaybwvTePSM0awakcF/7FgLX/3zAdkZqQwc8LwbmlnQ6wZx0lPOfxlnX9eFf/OhdmT4pfRXnVuPi+t2cWbG/ZwyRnt61Je08ALK0r53OSRDO6X2i31BKhpiPHi6l38YUUpb6wvI3tAKi9+4xMM7t99P0PkaCgA+qB/nHMmb2/cy70vrmfYwDS+evFYAHIH9+PGGafy6F82c8vHxnJm7qDWc+oam3hm6Xbue3E9tQ1N3HlZAdNOzebZZcX86t1tNLtzdWE+t18ynl8v2c5/vVLEpLzB3HDBmNb32FFey49eWs8lE3L41FmnYGZMG53NI18+j2v/ZzG3/XIZv7zlfMYMG8CrH+3mL0V7qKyLUR9rwh3uuGQ8F44f1q4tzy8vZvzwgZyTn9WurlfNe5tte2u4pnAUX5pxKmOCdZM6WrBqJxNGZDIuZyAAl0wYzpABaTy7rLhdALyxvoxvPfshuyrr+d+3NvPwTYWt5xyLXZV1XP/wYjaWVXPKoAy+cG4+zyzdzr8tWMM9V3UewiInihaD66PW7KjkuocX889zzuSa89q+kmFvVT0z732N2oYmLjtzONcUjqJodxU/f2szZQfqOX/sEP7tykmMH9724VdR00hjczPDBqYD0Nzs/PUTS3l9fRlPfvV8zj9tKAC3/WIZr67bzct/9wlGDenfrj57quq5Zt47FJfX0tjUjHv8UtdTBmWQnhJhR3ktlXUxnvs/FzLhlEwAfv7mJn7wx7VkpqfwbML+f3x+BU+9t52ZE3J4a8MeYs3OpLzB5Gf3I3dwP8bmDKDw1GyGDEhjxn8s4s7LCvjG5ae31uX7L6zmF+9sZcZpQ5lwSiZVdTF+vXQ744cP5NaLT+M///wRDbFm7r9+Kpd00mP5cHs5uYMzGD7o8Fdn7ayo47qHF7Orso6fXT+VmacPJxIx7v7TR8x7fSNPfvV8LuoQeD1tzY5KdlXWcdH4YQfdeyInr0MtBqcA6MNiTc2kRA/+T7x1bzVPvruN55YVsze4Mepj44fxN5eM44LThh40rNOZyrpGrnjgL2wqqyYzI4VBGamUlNfyrU9N4PZLxnd6TvH+Gv59wVrOOGUQl54xnLNGDmr9WaUVtVzxwF9IiUT43e0X8c6mvXz9qfe5ZEIOq3dUEo0Yv/2bi1i8aS/f+PUH/M3McXx71hnsqqzjqfe2sWzrfnaU11JaUUdNQ3x+Ii0aoaGpmRf/9uOcPiKztR57quq5d+E6Vu+oZP2uA9THmrnlY2P51qcmkJEapXh/Dbc+sYy1Oyv57mcm8uWLxrae+6t3t/FPv11JNGJcfuZwrps+mosLcohG2v+ZlVbUct1Di9lT1cBjN59H4ZghrcfqGpuY/dM3aWp2/vyNi+mfdmQd8YZYM79cvJVnlm7n0jOG87VPjDvkkFVlXSMD0lLa1e9AXSP/9UoRjU3NXD99NAUjMqmqj3HvwnU8/s4W3GHIgDSumJLHZyfnMnHkoNahNndnZ2Ud1fUxxg/P7PRnHi/uzq+XbCfW7HxpxqHvjWmINbN1bzWxZqep2cnMSOHUoZ33ELujTo+8tZnUaIT/5/zRnf5/O5zKukZK9te2640fDwoAOUhDrJk3N5QxPDODSfmDj/j8HeW1/GZpMftrGqiobaRfWpTvffaso/7NcWVxBVf/z9uMHtKfzXuqmTo6mye+Mp2NZVVcM+8dRmb1o6S8lrPzBvOrr57f6X82d6d4fy1Lt+7jvc37SU+J8N3PTjxkqDU1O9UNMQZltP8ArWmIcefTH/DSml3c9olx/MOsCTz13nb+6bcrmTkhhwkjMvnNsmL2VTeQn92P66aP5prCUeyvaeCXi7fy/PISDHj8lulMG33w110v3rSXax9azCvzMx4AAAshSURBVKcn5TLjtCGkRCP0S40yPDOd4YMyyO6fSmOT0xBrpj7WRLNDszubyqq598V1bN5TzekjBrJ+VxWDMlK4beY4brpgDAPS28LkuWXFfOd3qxgxKJ07Li3giikjWb6tnL975gN2lNeSEokH5PSxQ9i+r4adlXXcOONUPlaQw/PLi3l57S4am5zUqHH6iEyGDEhjbWkle6rivzRcXDCMv//kBCaPyqK6PsabG/awsqSc8cMHMmVUNmOG9j/kn/u+6gaWb93Ph8XlpKdEyMvuR352fyblDSYj9eB5ner6GHc9v7L1YoLvfPpMvnrxaa3Hq+pj/GllKa98tJs31pdR3dD+IoXpY4fw1Y+N5bIzRxwU1kfL3bln4ToefC1+7805+YO5+/PnMHFk+w/zusYmVpZUsLeqnvzs/ozK7s+e6nqeeHsLzy4rprqhibs/P4lrp4/ulnp1RgEgJ4U/ryrltl8uZ8KITJ657YLW32xfX1/GVx5bQla/VBbceTEjuhh+6Q5Nzc6//H4VT767jfPGZLNky34umZDDvBvOJT0lSkOsmRfX7OTJxdt4Z9NeohGjqdlJi0b4zDm53DZzXLueR0c/+MMafv7W5iOu17icAXzn0xOZOSGHNaWV3Pfiel75aDfZ/VO5+aKxfPG8Udy7cB2/WVbMeWOyqWloYvWOSnIHZ7Czso7RQ/rzo2smM2boAH6zrJin39vGwIwUvv+5szn31Law2lfdwDsb97KypIJVJRXsr2ngzNxBTMobTE1DEw+9sZH9NY2cNXIQG3ZV0dDhyqohA9L44nmjuPmiMQzPzKCusYlnlxXzy8Vb+Si4F6Plz6zF8Mx0br9kPNdOH0V6SpRYUzMrSyr49rMr2FhWxTc/OYE1Oyr548pS7vnCOVxdmM8fV5by/72wht0H6hkxKJ1LzxjB+WOHkJ4SIRIxtu6t5vG3t1JSXsvoIf353OSRzJmUy5m5mXy08wB/WrWT97ftZ+6UPD4/NY9IEBC7Kuv41bvbOGVwBpedMfygIb8fv7Seny7awHXTR3PR+KF8b/5q9tc08rFg+CxisLOynjU7KmhsOvhzNi0a4TOTcyk7UM9bRXv40TWTuXJq/hH/e0iGAkBOGu9v28/YYQPI6p/Wbv+SLfvI6pdKwWE+VLubu/OzV4q476X1zJyQw7wvndvpb6hFu6t4bnkxWf1SuercfIYG8yVdKa9poLHJiTU3U9PQxO7KenYfqKO8ppHUaIT0lAipKRFSIkbEICM1ykXjh5HaofezfNt+HniliEUf7QbADP7vJeP5+mUFRCPGy2t3879/2cz44QP5h1lntOspHK0DdY08+tYWXl23m/PGZHPpGSOYOjqLzXuq+XB7Oa+vL2Ph6p2kRCNcfuZwFm/ax77qBiblDWb2pFM4d3Q2k0dl4Q4l5bVsKqvikbc28+7mfeQOziA/ux+rSiqpbWxiyIA0/uu6qVw0fhj1sSa++vhS/lK0hymjsli+rZyz8wbxL585i/PGZHfa64g1NbNw9S6efHcrizftpdlhUEYKlXUxzGDk4HjvcuroLL71qQm8vq6Mx97eQn2sLdQmj8piXM4A+qdFOVAX4/cf7ODqc/P5zy+cQyRilNc08MOF63h/WzlO/N/O4H6pTDs1m2mjs8kdnEHx/hq276vFca6cmk9OZjp1jU185bElLN60lx9/cQqfnpTbrndbH2tiT1UDQ/qnHfVihgoAkWNQtPsApw4dcNAHb2+zZkclv3pvK5866xQuLuj5b43bvKeah97YxPwPSphx2lD++uOncf7YIYccGnJ33tm4lwdeK6KusZlz8gczOT+LiwuGtQvVmoYYNzzyHut2HuDvP3k6N1wwJumhnT1V9by4ehdLt+6j8NQh/NXEEQwdkMZv3y/hP/70EXuq6jGDK6fk8Y3LT6emMcbLa3bxyke72X2gnpqGJmoaYlw5NY8fXDGpW4aUahpi3PToeyzZsh+A7P6pZGakUl7TQGVdfKn3J74ynY8f5TcBKgBEpE9pbGqmPtbMwG7ozbSorGvkDx+Wcu6p2a1XnZ0o1fUxXvhwBzsr69hTVU9lbYzs/qkMG5jOsMx0PnF6DiOTXBqmo0MFgO4DEJGTUmo00u09skEZqVx//vGbjD2cAekpx3UiuDO9uz8rIiLHjQJARCSkkgoAM5tlZuvMrMjM7urkuJnZ/cHxFWY2ratzzWyImb1kZhuC54MvlhYRkeOmywAwsyjwADAbmAhcZ2YTOxSbDRQEj1uBB5M49y5gkbsXAIuCbREROUGS6QFMB4rcfZO7NwBPA3M7lJkLPOFxi4EsM8vt4ty5wOPB68eBK46xLSIicgSSCYA8YHvCdnGwL5kyhzt3hLuXAgTPna4TbGa3mtlSM1taVlaWRHVFRCQZyQRAZ3c5dLx54FBlkjn3sNz9IXcvdPfCnJyev7FFRKSvSCYAioFRCdv5QMevdzpUmcOduysYJiJ43p18tUVE5FglcyPYEqDAzMYCJcC1wPUdyswH7jCzp4HzgQp3LzWzssOcOx+4Cbg7eP59VxVZtmzZHjPbmkSdOzMM2HOU557MwtjuMLYZwtnuMLYZjrzdna6f3WUAuHvMzO4AFgJR4FF3X21mtwXH5wELgDlAEVAD3Hy4c4O3vht4xsxuAbYBVydRl6MeAzKzpZ3dCt3XhbHdYWwzhLPdYWwzdF+7k1oKwt0XEP+QT9w3L+G1A7cne26wfy9w2ZFUVkREuo/uBBYRCakwBcBDPV2BHhLGdoexzRDOdoexzdBN7T6ploMWEZHuE6YegIiIJAhFAHS1mF1fYGajzOxVM1trZqvN7M5gf59fdM/Momb2vpn9IdgOQ5uzzOxZM/so+Du/oK+328z+Nvi3vcrMnjKzjL7YZjN71Mx2m9mqhH2HbKeZ/WPw2bbOzD51JD+rzwdAkovZ9QUx4JvufiYwA7g9aGcYFt27E1ibsB2GNv8U+LO7nwFMJt7+PttuM8sDvg4UuvvZxC8rv5a+2ebHgFkd9nXazuD/+LXAWcE5/x185iWlzwcAyS1md9Jz91J3Xx68PkD8AyGPPr7onpnlA58Gfp6wu6+3eRDwceARAHdvcPdy+ni7iV+23s/MUoD+xFcV6HNtdvc3gH0ddh+qnXOBp9293t03E78Xa3qyPysMAZDMYnZ9ipmNAaYC75LkonsnsZ8A3waaE/b19TafBpQB/xsMff3czAbQh9vt7iXAvcRvGi0lvtrAi/ThNndwqHYe0+dbGALgmBekO5mY2UDgOeAb7l7Z0/U5nszsM8Bud1/W03U5wVKAacCD7j4VqKZvDH0cUjDmPRcYC4wEBpjZl3q2Vr3CMX2+hSEAklnMrk8ws1TiH/5Puvvzwe6+vOjeRcDnzGwL8aG9S83sl/TtNkP833Sxu78bbD9LPBD6crsvBza7e5m7NwLPAxfSt9uc6FDtPKbPtzAEQOtidmaWRnzCZH4P16nbmZkRHxNe6+4/SjjUsugeJLno3snC3f/R3fPdfQzxv9dX3P1L9OE2A7j7TmC7mU0Idl0GrKFvt3sbMMPM+gf/1i8jPs/Vl9uc6FDtnA9ca2bpwaKbBcB7Sb+ru/f5B/GF6tYDG4F/7un6HKc2fox4128F8EHwmAMMJX7VwIbgeUhP1/U4tX8m8IfgdZ9vMzAFWBr8ff8OyO7r7Qa+D3wErAJ+AaT3xTYDTxGf52gk/hv+LYdrJ/DPwWfbOmD2kfws3QksIhJSYRgCEhGRTigARERCSgEgIhJSCgARkZBSAIiIhJQCQEQkpBQAIiIhpQAQEQmp/x85pprOn0yQ6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_per_epoch = model.history.history['loss']\n",
    "plt.plot(range(len(loss_per_epoch)),loss_per_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By Now, the Model has Learnt how to predict the 13th Month based on the values of the previous 12 months. \n",
    ">Now we have to Test it by forecasting into the Test Domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_eval_batch = scaled_train[-12:]\n",
    "\n",
    "first_eval_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 12, 1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "shaped_first_eval_batch = first_eval_batch.reshape((1,n_input,n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 12, 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shaped_first_eval_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's predict the First value of test domain, using last 12 values of train domain.\n",
    "yptest0 = model.predict(shaped_first_eval_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.61036694, 0.6384039900249376)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yptest0[0][0] , scaled_test[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PRETTY CLOSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Similarly we'll devlop a FOR-LOOP to forecast all test values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generalized Predictive For Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = []\n",
    "\n",
    "first_eval_batch = scaled_train[-n_input:]\n",
    "current_batch = first_eval_batch.reshape((1,n_input,n_features))\n",
    "\n",
    "\n",
    "for i in range(len(test)):\n",
    "    \n",
    "    current_pred = model.predict(current_batch)[0][0]\n",
    "    \n",
    "    test_preds.append(current_pred)\n",
    "    \n",
    "    #Update current batch to the most recent 12 values\n",
    "    #so that we can predict the 13th one.\n",
    "    \n",
    "    current_batch = np.append(current_batch[:,1:,:] , [[[current_pred]]], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Predicted values are in MinMaxScaled range. \n",
    "\n",
    "> So we gotta invert them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.63840399],\n",
       "        [0.59351621]]),\n",
       " [0.61036694, 0.5970134])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A sneak peak into the correctness of eval.\n",
    "scaled_test[:2] , test_preds[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_preds = scaler.inverse_transform([test_preds])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-63-961d49a1c598>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test['LSTM-Forecasts'] = actual_preds.reshape(24,1)\n"
     ]
    }
   ],
   "source": [
    "test['LSTM-Forecasts'] = actual_preds.reshape(24,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Thousands of Passengers</th>\n",
       "      <th>LSTM-Forecasts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>360</td>\n",
       "      <td>348.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>342</td>\n",
       "      <td>343.402379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>406</td>\n",
       "      <td>359.048515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>396</td>\n",
       "      <td>363.433358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>420</td>\n",
       "      <td>392.867316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>472</td>\n",
       "      <td>458.570501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>548</td>\n",
       "      <td>518.371147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>559</td>\n",
       "      <td>522.232142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>463</td>\n",
       "      <td>446.157754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>407</td>\n",
       "      <td>369.793633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>362</td>\n",
       "      <td>329.816405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>405</td>\n",
       "      <td>339.226005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>417</td>\n",
       "      <td>349.382095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>391</td>\n",
       "      <td>351.894569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>419</td>\n",
       "      <td>358.794347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>461</td>\n",
       "      <td>370.970326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>472</td>\n",
       "      <td>411.363893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>535</td>\n",
       "      <td>479.570015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>622</td>\n",
       "      <td>539.856602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>606</td>\n",
       "      <td>544.832265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>508</td>\n",
       "      <td>473.947268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>461</td>\n",
       "      <td>385.829459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>390</td>\n",
       "      <td>339.800954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>432</td>\n",
       "      <td>340.600817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Thousands of Passengers  LSTM-Forecasts\n",
       "Month                                              \n",
       "1959-01-01                      360      348.757143\n",
       "1959-02-01                      342      343.402379\n",
       "1959-03-01                      406      359.048515\n",
       "1959-04-01                      396      363.433358\n",
       "1959-05-01                      420      392.867316\n",
       "1959-06-01                      472      458.570501\n",
       "1959-07-01                      548      518.371147\n",
       "1959-08-01                      559      522.232142\n",
       "1959-09-01                      463      446.157754\n",
       "1959-10-01                      407      369.793633\n",
       "1959-11-01                      362      329.816405\n",
       "1959-12-01                      405      339.226005\n",
       "1960-01-01                      417      349.382095\n",
       "1960-02-01                      391      351.894569\n",
       "1960-03-01                      419      358.794347\n",
       "1960-04-01                      461      370.970326\n",
       "1960-05-01                      472      411.363893\n",
       "1960-06-01                      535      479.570015\n",
       "1960-07-01                      622      539.856602\n",
       "1960-08-01                      606      544.832265\n",
       "1960-09-01                      508      473.947268\n",
       "1960-10-01                      461      385.829459\n",
       "1960-11-01                      390      339.800954\n",
       "1960-12-01                      432      340.600817"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b69b9c250>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "test.plot(figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional- Visual Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training Preds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainpreds = model.predict(generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trainpreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "actualtrainpreds = scaler.inverse_transform(trainpreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LSTM-Fit'] = np.empty(shape=(len(df),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-87-4263c9077e26>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['LSTM-Fit'][:108] = actualtrainpreds.reshape(108)\n"
     ]
    }
   ],
   "source": [
    "df['LSTM-Fit'][:108] = actualtrainpreds.reshape(108)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Thousands of Passengers</th>\n",
       "      <th>LSTM-Fit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "      <td>1.239158e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "      <td>1.273263e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "      <td>1.416507e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "      <td>1.451898e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "      <td>1.376197e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>606</td>\n",
       "      <td>1.162438e-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>508</td>\n",
       "      <td>1.162487e-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>461</td>\n",
       "      <td>1.162536e-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>390</td>\n",
       "      <td>1.162586e-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>432</td>\n",
       "      <td>1.162635e-319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Thousands of Passengers       LSTM-Fit\n",
       "Month                                             \n",
       "1949-01-01                      112   1.239158e+02\n",
       "1949-02-01                      118   1.273263e+02\n",
       "1949-03-01                      132   1.416507e+02\n",
       "1949-04-01                      129   1.451898e+02\n",
       "1949-05-01                      121   1.376197e+02\n",
       "...                             ...            ...\n",
       "1960-08-01                      606  1.162438e-319\n",
       "1960-09-01                      508  1.162487e-319\n",
       "1960-10-01                      461  1.162536e-319\n",
       "1960-11-01                      390  1.162586e-319\n",
       "1960-12-01                      432  1.162635e-319\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf = pd.conc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-92-0f9ba1c57a0f>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['LSTM-Fit'][108:] = np.nan\n"
     ]
    }
   ],
   "source": [
    "df['LSTM-Fit'][108:] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b6be18790>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Thousands of Passengers</th>\n",
       "      <th>LSTM-Forecasts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>360</td>\n",
       "      <td>348.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>342</td>\n",
       "      <td>343.402379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>406</td>\n",
       "      <td>359.048515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>396</td>\n",
       "      <td>363.433358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>420</td>\n",
       "      <td>392.867316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>472</td>\n",
       "      <td>458.570501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>548</td>\n",
       "      <td>518.371147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>559</td>\n",
       "      <td>522.232142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>463</td>\n",
       "      <td>446.157754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>407</td>\n",
       "      <td>369.793633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>362</td>\n",
       "      <td>329.816405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>405</td>\n",
       "      <td>339.226005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>417</td>\n",
       "      <td>349.382095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>391</td>\n",
       "      <td>351.894569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>419</td>\n",
       "      <td>358.794347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>461</td>\n",
       "      <td>370.970326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>472</td>\n",
       "      <td>411.363893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>535</td>\n",
       "      <td>479.570015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>622</td>\n",
       "      <td>539.856602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>606</td>\n",
       "      <td>544.832265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>508</td>\n",
       "      <td>473.947268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>461</td>\n",
       "      <td>385.829459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>390</td>\n",
       "      <td>339.800954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>432</td>\n",
       "      <td>340.600817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Thousands of Passengers  LSTM-Forecasts\n",
       "Month                                              \n",
       "1959-01-01                      360      348.757143\n",
       "1959-02-01                      342      343.402379\n",
       "1959-03-01                      406      359.048515\n",
       "1959-04-01                      396      363.433358\n",
       "1959-05-01                      420      392.867316\n",
       "1959-06-01                      472      458.570501\n",
       "1959-07-01                      548      518.371147\n",
       "1959-08-01                      559      522.232142\n",
       "1959-09-01                      463      446.157754\n",
       "1959-10-01                      407      369.793633\n",
       "1959-11-01                      362      329.816405\n",
       "1959-12-01                      405      339.226005\n",
       "1960-01-01                      417      349.382095\n",
       "1960-02-01                      391      351.894569\n",
       "1960-03-01                      419      358.794347\n",
       "1960-04-01                      461      370.970326\n",
       "1960-05-01                      472      411.363893\n",
       "1960-06-01                      535      479.570015\n",
       "1960-07-01                      622      539.856602\n",
       "1960-08-01                      606      544.832265\n",
       "1960-09-01                      508      473.947268\n",
       "1960-10-01                      461      385.829459\n",
       "1960-11-01                      390      339.800954\n",
       "1960-12-01                      432      340.600817"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.append(x1,x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b6dbee9a0>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(x1,df['Thousands of Passengers'],label='Actual Data(Historical)')\n",
    "plt.plot(x1,df['LSTM-Fit'],label='LSTM-Fit')\n",
    "\n",
    "plt.plot(x2,test['Thousands of Passengers'],label='Actual Data(Future/Unseen)')\n",
    "plt.plot(x2,test['LSTM-Forecasts'],label='LSTM-Forecasts')\n",
    "\n",
    "plt.axvline(x=x2[0],color='black')\n",
    "plt.ylabel('Thousands of Airline Passengers')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation on UNSEEN - MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "ac = test['Thousands of Passengers']\n",
    "preds = test['LSTM-Forecasts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = mean_squared_error(ac,preds)**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.623822801099415"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*rmse/np.mean(ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#11% Error. Not too Bad. \n",
    "#89% Accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
